动态动力学评估偏好组分多样的多主元合金纳米颗粒用于析氢反应
Dynamic Kinetic Evaluation Favors Compositionally Diverse Multicomponent Alloy Nanoparticles for Hydrogen Evolution
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中文总结 AI 辅助
本研究通过结合机器学习、蒙特卡洛模拟与动力学评估，发现多组分高熵合金纳米颗粒在析氢反应中优于静态吸附能筛选的富Pt低组分组合，提出动力学筛选可补充传统静态筛选方法。
中文摘要 AI 辅助
高熵合金（HEA）纳米颗粒提供化学多样性的催化位点，但在其庞大设计空间中识别最优组分仍具挑战性。我们开发了一个结合机器学习能量预测、热力学蒙特卡洛采样、贝叶斯优化和动力学蒙特卡洛（kMC）模拟的框架，用于在九元素组分空间中筛选85原子纳米颗粒模型以用于析氢反应。基于吸附能的静态优化选择了富Pt、低组分的组合，而基于kMC的优化将多元素组分Au$_{33}$Co$_{20}$Cu$_{6}$Pd$_{26}$评为评估候选中的最高。循环分辨分析进一步显示氢在Co/Pd富集环境中的吸附、通过中间结合位点的迁移以及较弱结合含Au环境中的频繁Heyrovsky脱附。这一路径级证据支持多位点反应图景，并说明纳入动力学可补充静态吸附能筛选。
英文摘要
High-entropy alloy (HEA) nanoparticles offer chemically diverse catalytic sites, but identifying optimal compositions across their large design space remains challenging. We developed a framework combining machine-learning energy prediction, thermodynamic Monte Carlo sampling, Bayesian optimization, and kinetic Monte Carlo (kMC) simulations to screen 85-atom nanoparticle models across a nine-element composition space for the hydrogen evolution reaction. Static optimization based on adsorption energies selected Pt-rich, low-component compositions, whereas kMC-based optimization ranked the multielement composition Au$_{33}$Co$_{20}$Cu$_{6}$Pd$_{26}$ highest among the evaluated candidates. Cycle-resolved analysis further showed hydrogen adsorption at Co/Pd-rich environments, migration through intermediate-binding sites, and frequent Heyrovsky desorption at weaker-binding, Au-containing environments. This pathway-level evidence supports a multisite reaction picture and illustrates how incorporating kinetics can complement static adsorption-energy screening.
发表机构
- The University of Tokyo（东京大学）
- National Institute for Materials Science（物质材料研究机构）
- National Institute of Advanced Industrial Science and Technology（产业技术综合研究所）
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